system

Abstract

We provide the system. [Solution] A means for receiving an electronic document using an information processing device and analyzing the electronic document using a natural language processing engine, An information processing device receives data and analyzes the data using natural language processing means. Based on the analyzed information, a means to identify risk factors and generate legal advice, Means for providing the generated legal advice to the output device, A means of providing a function for users to photograph or transmit contracts using an information terminal, A means for automatically analyzing relevant documents and generating additional information based on risk assessment results, A system that includes this.

Description

Technical Field

[0006] ,

[0001] The technology of this disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] It is an object to solve the problems of personalization, quality variation, man-hour bias, and human error in legal work, and to avoid stagnation of work due to the difficulty of global legal response.

Means for Solving the Problems

[0005] By using an information processing device to receive an electronic document and analyze the document with a natural language processing engine to identify risk elements and generate legal advice, and constructing a system to output this on a display device, the efficiency of legal work and consistent quality are realized. Furthermore, by appropriately converting the format of the electronic document and providing related information to the user, global legal response is also made possible.

[0006] An "information processing device" is a computer system used to receive, analyze, and process electronic documents.

[0007] An "electronic document" is a document expressed in a digital format such as text or PDF.

[0008] A "natural language processing engine" is a program that allows machines to understand and analyze documents written in human language.

[0009] A "risk element" refers to a potential legal problem or issue present within a contract or legal document.

[0010] "Legal advice" refers to expert guidance on specific legal situations or documents.

[0011] A "display device" is a device used to visually display electronic information. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8]It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the tagged RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by a processor.

[0017] In the following embodiments, the tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0018] In the following embodiments, the tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0020] [First Embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention provides a system that allows users to upload contracts via their devices and evaluates the legal risks associated with those contracts using AI technology. The operation of this system's program is described below.

[0034] First, the user operates the terminal to select the contract and upload it to the system. The file formats the user can use are PDF and Word files, which are commonly used and therefore convenient. Then, the user clicks a button to request a review of the contract.

[0035] The server processes electronic documents received from users. First, the server converts the electronic documents into a format that is easy to parse. This process activates a natural language processing engine, which meticulously analyzes the contents of the contract. Specifically, it segments the document by clause, and each segment is tagged. For example, the server identifies confidentiality clauses and limitation of liability clauses and assesses the risks associated with each.

[0036] Subsequently, the AI ​​on the server assesses the legal risks. The AI ​​accesses legal databases and identifies risks for each contract clause, taking into account the latest laws and precedents. This process uses machine learning models pre-trained by experienced legal professionals. If the server determines that the timeframe is inappropriate, it will generate advice such as, "The timeframe in the contract is unclear. We recommend specifying a concrete timeframe."

[0037] Finally, the generated legal advice is sent to the user's device. The user can review the advice on their device and revise the contract as needed. This entire process makes it possible to identify and correct legal deficiencies early. In addition, the system supports global legal requirements and can be used to review contracts in different jurisdictions. In this way, it improves the efficiency of legal operations and enhances risk management.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] Users use their devices to select contract files they want to review and upload them to the system. File formats include PDF and Word.

[0041] Step 2:

[0042] The server receives electronic documents uploaded by users. It then converts the files into a format suitable for text analysis. If the file is a PDF, it uses an open-source PDF conversion library to extract the text.

[0043] Step 3:

[0044] The server activates a natural language processing engine to analyze the contents of the contract. The document is divided into clauses, and clauses such as confidentiality clauses and limitation of liability clauses are tagged. Morphological analysis and sentence parsing techniques are used in this process.

[0045] Step 4:

[0046] The server uses an AI model to evaluate the legal risks in each clause. The AI ​​has previously learned from legal databases and case precedents, and uses this information to scrutinize the contract. If necessary, it performs a risk assessment from a legal perspective.

[0047] Step 5:

[0048] The server automatically generates specific legal advice based on the evaluation results. For example, if the confidentiality clause does not specify a protection period, it will generate advice such as, "It is recommended that the protection period be clearly defined."

[0049] Step 6:

[0050] The generated legal advice is sent from the server to the terminal. The terminal receives it and displays it so that the user can review it.

[0051] Step 7:

[0052] Users can review the advice provided on their device and revise the contract as needed. They can also re-upload the edited content for further analysis.

[0053] (Example 1)

[0054] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0055] There is a challenge in quickly and accurately assessing the legal risks of electronic documents and providing users with appropriate legal advice. In particular, complex legal documents such as contracts have diverse interpretations of clauses, making manual analysis time-consuming and laborious, and making it difficult to reflect the latest legal information. Furthermore, a lack of knowledge of relevant laws and case law can lead to inadequate risk management.

[0056] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0057] This invention includes a server that receives an electronic document, identifies its format, and converts it into an appropriate processing format; a server that uses a natural language processing engine to segment the converted electronic document clause by clause, tags it, and analyzes it; and a server that accesses a legal database and utilizes a machine learning module to identify risk components and generate legal advice based on the analyzed content. This enables the rapid and accurate assessment of legal risks within electronic documents, allowing users to receive timely and accurate legal advice.

[0058] An "information processing device" is a device that has the function of receiving electronic documents, identifying their format, and converting them into an appropriate format.

[0059] A "natural language processing engine" is software that uses computers to analyze human language, structure the text, and understand it.

[0060] A "legal database" is a digital archive in which legal information, including laws and precedents, is organized and stored in a searchable format.

[0061] A "machine learning module" is software that uses algorithms to learn patterns based on data and predict or classify new information.

[0062] "Communication equipment" refers to hardware or software used to send and receive data over a network.

[0063] A "risk component" refers to an element in a contract's clauses or conditions that involves legal risks.

[0064] "Legal advice" refers to suggestions and proposals to minimize risks based on contracts and laws.

[0065] A "user device" is a device operated by the user who ultimately receives the information, and typically includes computers and smartphones.

[0066] This invention relates to a legal document analysis system. Users upload electronic documents, such as contracts, to the system using a terminal. In this process, the terminal receives PDF or Word files provided by the user and sends them to the server.

[0067] The server processes the received electronic documents. First, it identifies the format of the electronic document and converts it into a parsable text format using document processing tools such as Apache® Tika. Next, the server uses a natural language processing engine (e.g., NLTK or spaCy) to segment the text data by clause and tag it according to the characteristics of the legal document.

[0068] Next, the server uses AI to access legal databases and assess the legal risks of the analyzed clauses. Here, a pre-trained machine learning module (for example, a legally specialized BERT model) is used to identify risk elements for each clause and generate legal advice. Based on the AI's assessment, specific advice is generated—for example, "The duration in the contract is unclear. We recommend specifying a concrete duration."

[0069] The generated legal advice is sent to the user's terminal via the server, and the user can review the advice through a display device. This allows the user to review the contents of the contract and make adjustments as needed.

[0070] As a concrete example, when a retailer enters into a new business agreement with an overseas supplier, uploading the contract to a terminal allows the server to detect risks based on local laws and international standards and provide advice. This approach enables companies to smoothly conclude contracts and mitigate risks.

[0071] An example of a prompt for a generative AI model might be, "Evaluate the legal risks of the following contract clause: The contract term is not specified." This would allow the system to identify and suggest specific risks and areas for improvement.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The user selects a contract on the terminal and begins uploading. PDF and Word format electronic documents are selected as input. The terminal then receives the user's input and prepares to send the specified file to the server. The output is the file that has been sent to the server.

[0075] Step 2:

[0076] The server identifies the format of the received electronic document and converts it into a text format that is easy to parse. Specifically, the server uses a document processing tool such as Apache Tika to verify the input file format and then performs data processing to convert it into text data. The output is text data suitable for segmentation and analysis.

[0077] Step 3:

[0078] The server processes the converted text using a natural language processing engine, segmenting and tagging it according to each contract clause. The input is the contract content in text format. The server uses engines such as NLTK and spaCy to extract clauses and perform specific data calculations, such as tagging confidentiality clauses and limitation of liability clauses. The output is data for each tagged clause.

[0079] Step 4:

[0080] The server uses an AI model to assess legal risks based on tagged data. Specifically, it receives tagged clause data as input. The server accesses a legal database and performs data calculations using a trained AI model such as BERT to analyze the risks associated with each clause. This process generates legal advice. The output is a set of risk assessment results and advice based on them.

[0081] Step 5:

[0082] The server sends the generated legal advice to the terminal and displays it to the user. The generated advice data is used as input. The server performs specific data transfer operations, sending the data to the terminal via communication means, allowing the user to view the advice through the display device. The output is legal advice information in a format viewable by the user.

[0083] (Application Example 1)

[0084] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0085] In recent years, there has been a growing need to quickly and accurately assess legal risks in contract document reviews. This process has traditionally relied on manual checks by experts, which is not only time-consuming and laborious but also prone to human error. Furthermore, the need to comply with the differing legal regulations of various jurisdictions in a global business environment adds further complexity. Therefore, there is a demand for a system that can quickly and efficiently handle everything from contract uploads to risk assessments and the provision of legal advice.

[0086] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0087] In this invention, the server includes means for receiving data using an information processing device and analyzing the data using natural language processing means; means for identifying risk factors and generating legal advice based on the analyzed content; means for providing the generated legal advice to an output device; means for providing a function for the user to photograph or transmit the contract using an information terminal; and means for automatically analyzing relevant documents and generating additional information based on the risk assessment results. This makes it possible to efficiently assess the legal risks of a contract and provide appropriate advice quickly, as well as to realize comprehensive support that can respond to a variety of legal situations.

[0088] An "information processing device" is a device that has the ability to receive data and analyze or transform it.

[0089] "Data" refers to various types of electronic information received by information processing devices, and in particular includes electronic documents such as contracts.

[0090] "Natural language processing" refers to technologies used to analyze data in the form of natural language, which is human language, in order to make its content easier to understand.

[0091] A "risk factor" refers to an element within a contract or document that may contain potential legal issues or risks.

[0092] "Legal advice" refers to legal advice or suggestions that are generated based on risk factors.

[0093] An "output device" is a device used to provide analysis results and legal advice, and to display them in a format that can be reviewed by the user.

[0094] An "information terminal" refers to an electronic device used by a user to input or manipulate data.

[0095] "Photography" is the act of converting a physical document into digital data using a camera, scanner, or other device.

[0096] "Transmission" refers to the act of transferring data from one device to another via a communication line.

[0097] "Risk assessment results" refer to the evaluation results of risk factors in the analyzed data, and serve as legal basis for judgments.

[0098] "Related documents" refer to additional information or documents that are referenced to complement or support the risk assessment results.

[0099] "Automatic analysis" refers to the process by which a computer system analyzes data without human intervention.

[0100] "Additional information" refers to relevant information and explanations provided to complement the generated legal advice.

[0101] The system that realizes this invention begins with the user uploading a contract. It provides the user with the ability to photograph or send the contract using an information terminal. Smartphones and tablets are commonly used as information terminals.

[0102] The received data is processed on the server by an information processing device. The server analyzes this data using natural language processing. Specifically, it uses the Google® Cloud Natural Language API to analyze the clauses within the contract and perform natural language processing to understand their structure. Through this analysis, each segment within the contract is identified, and risk factors are extracted.

[0103] Next, based on the analyzed data, risk factors are evaluated using machine learning methods, and legal advice is generated. In this step, a machine learning model built using TENSORFLOW® identifies high-risk elements by comparing them with a legal database and creates necessary advice. This legal advice is generated as appropriate by a generative AI model.

[0104] The generated legal advice is provided to the user via an output device. The user can view this information on their smartphone or computer screen and make any necessary corrections.

[0105] For example, if a company enters into a sales contract for a new product and the contract term clause is inappropriate, the system will provide legal advice such as, "The contract term is ambiguous. Please specify a concrete term." An example of a prompt to the generating AI model would be, "Identify potentially problematic clauses in the contract and assess the legal risks."

[0106] This system enables users to quickly and efficiently assess the legal risks of contracts, leading to increased efficiency in legal work and improved risk management.

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The user uses a terminal to take a picture of the contract or select an existing electronic file and upload it to the system. The input is the captured image data or electronic document, and the output is the data sent to the server.

[0110] Step 2:

[0111] The server checks the format of the received files and converts different formats, such as PDFs, Word files, and image data, into a format that can be parsed. The input is data sent by the user, and the output is text data that is easy to process with natural language processing.

[0112] Step 3:

[0113] The server uses the Google Cloud Natural Language API to analyze text data, identify each clause within the contract, and perform natural language processing. The input is a text-based contract, and the output is a collection of sentences divided into chapters and sections.

[0114] Step 4:

[0115] The server evaluates risk factors using a machine learning model based on the analysis results, employing TensorFlow. The input is the contract details, divided by chapter and section, and the output is a list of identified risk factors.

[0116] Step 5:

[0117] The server generates legal advice based on risk factors. It utilizes a generative AI model to create specific advice. The input is a list of risk factors, and the output is a written legal advice document.

[0118] Step 6:

[0119] The server generates legal advice and sends it to the terminal, providing it to the output device for user review. The input is the legal advice text, and the output is the advice message displayed on the user's terminal.

[0120] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0121] This invention is a system in which a user uploads a contract via a terminal, and AI technology is used not only to assess the legal risks but also to recognize the user's emotions and adjust the advice accordingly. The operation of this system's program is described below.

[0122] Users select the contract they want to review via their device and upload it to the system. The device checks the format of the contract file (such as PDF or Word) and converts it to a format suitable for analysis as needed. This enables efficient analysis of the contract content using a natural language processing engine.

[0123] The server analyzes the converted electronic document using a natural language processing engine, segmenting the contract content and identifying risk elements based on the clauses. The AI ​​model generates legal advice regarding the identified risks based on legal databases and case law. The server then sends this advice to a display device so that the user can view it on their device.

[0124] Furthermore, the emotion engine recognizes the user's emotional state based on their interaction with the system. Specifically, it analyzes the context and typing speed of the user's questions, and, if voice input is available, the tone of their voice. Based on these recognition results, the server adjusts the wording of the generated legal advice. For example, if the user is feeling stressed, the system will provide simple and clear advice.

[0125] Furthermore, based on the emotions the emotion engine recognizes, it can personalize and provide relevant additional information and suggestions. For example, if a user expresses anxiety, the server can provide links to frequently asked questions (FAQs) or beginner's guides.

[0126] This system not only allows for proper management of legal risks, but also enables the provision of support tailored to the user's emotional state, resulting in a more personalized user experience.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] Users use their devices to select contract files to be reviewed and upload them to the system. PDF and Word files are commonly used as contract formats.

[0130] Step 2:

[0131] The terminal sends the uploaded electronic document to the server. The server receives the electronic document and converts it into a format that can be processed. For example, it prepares data for analysis by converting a PDF to text format.

[0132] Step 3:

[0133] The server uses a natural language processing engine to analyze the contract. This analysis divides the document into clauses and identifies confidentiality clauses and limitation of liability clauses. Each segment within the document is tagged, and risks from a legal perspective are extracted.

[0134] Step 4:

[0135] The AI ​​on the server references a legal database and assesses risk factors. Based on pre-trained data, the AI ​​model generates legal advice regarding the risks. For example, if the deadline setting is inappropriate, it might generate advice such as, "We recommend reviewing the protection period settings."

[0136] Step 5:

[0137] The server activates the emotion engine and analyzes the user's emotional state. Based on the user's interactions and input data, it evaluates emotions such as stress and anxiety. If voice input is available, voice tone is also used in the analysis.

[0138] Step 6:

[0139] The server adjusts the generated legal advice to suit the user's emotional state. For example, if the user is expressing anxiety, it prioritizes presenting concise and clear advice.

[0140] Step 7:

[0141] The server sends the adjusted legal advice to the device. The device displays the received advice to the user, who can review it and revise the contract. Personalized suggestions and relevant information based on emotions are also provided.

[0142] (Example 2)

[0143] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0144] Traditional contract analysis systems provide the functionality to legally evaluate the content of documents and identify risks, but they are unable to provide information tailored to the user's emotional state. Therefore, they fail to meet the need to alleviate user anxiety and stress and to offer more personalized advice.

[0145] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0146] In this invention, the server includes means for receiving an electronic document using an information processing device and analyzing the electronic document using a natural language processing engine; means for recognizing the user's emotional state based on user input information and adjusting the expression of legal advice based on the recognized emotional state; and means for providing additional or relevant information according to the user's emotional state. This makes it possible to provide personalized advice tailored to the user's emotional state.

[0147] An "information processing device" is a computer system that receives electronic documents and performs specified processing.

[0148] An "electronic document" is document data composed in a digital format, including file formats such as contracts and reports.

[0149] A "natural language processing engine" is a software technology that analyzes human language and converts it into a format that a computer can understand.

[0150] "Risk elements" refer to parts of an electronic document that are legally important and contain uncertainties or potential problems.

[0151] "Legal advice" refers to guidance and support information provided based on legal standards regarding actions users should take and considerations they should make in specific situations.

[0152] A "display device" is a hardware device, such as a screen or monitor, used to provide information to a user visually.

[0153] "Emotional state" refers to the mental or emotional response a user exhibits at a particular point in time during an interaction.

[0154] "Additional information" refers to supplementary information provided to the user, including data and materials to facilitate a more detailed understanding.

[0155] To implement this invention, the user first selects the contract document they wish to review using a terminal and uploads it electronically to the system. The terminal checks the format of the uploaded document and, if necessary, uses an automatic conversion tool to convert it into a data format that can be properly analyzed, and then sends it to the server as an electronic document. For example, this process includes converting a PDF to a text format.

[0156] The server analyzes received electronic documents using a natural language processing engine. This engine divides the document into segments and identifies the legal elements contained within each segment. An AI model then generates legal advice for the identified risks based on legal databases and past case precedents. This AI model has accumulated a wealth of data on common risks and their countermeasures, enabling sophisticated assessments.

[0157] Once the analysis and advice generation are complete, the server uses an emotion engine to recognize the user's emotional state based on factors such as input speed and tone of voice. Based on this, the server adjusts the wording of the generated legal advice according to the user's emotional state. Furthermore, the server can also provide additional relevant information and legal information to help the user better understand the situation.

[0158] As a concrete example, consider a case where a user uploads a lease agreement. This system would have a server identify important clauses and risks related to the lease agreement (e.g., termination clauses) and provide the user with simple and clear advice. If the server determines that the user is showing anxiety or stress, it would also provide links to beginner-friendly guide information to help them understand with confidence.

[0159] An example of a prompt for using a generative AI model is, "I have uploaded a residential lease agreement. Please assess the risks associated with termination and provide simple advice to users who are feeling stressed." This prompt allows the system to provide assessments and advice tailored to specific user requests.

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The user uses a terminal to select the contract they want to review and upload it to the system. The input is the contract file, and the output is the transfer of the file to the system. Specifically, the user selects the contract through a file selection dialog and presses the "Upload" button.

[0163] Step 2:

[0164] The terminal checks the format of the uploaded contract. The input is the file uploaded by the user, and the output is its conversion into a parseable data format. Specifically, if a contract in PDF format is uploaded, the automatic conversion tool converts it to text format and saves it as a string.

[0165] Step 3:

[0166] The server passes the converted electronic document to a natural language processing engine for analysis. The input is contract data converted to text format, and the output is segmented content of the contract. Specifically, the natural language processing engine syntactically analyzes the contract and extracts important keywords and phrases for each clause.

[0167] Step 4:

[0168] The server uses an AI model to assess the legal risks of segmented contract content and generate advice. The input consists of segmented contract content and information from a legal database; the output is legal advice. Specifically, the AI ​​model determines the degree of risk and documents the advice based on relevant case law and regulations.

[0169] Step 5:

[0170] The server sends the generated advice to the terminal, which then displays the advice on its screen. The input is advice data generated by the AI ​​model, and the output is advice information presented visually to the user. Specifically, the terminal displays the advice in a text window so that the user can easily review it.

[0171] Step 6:

[0172] The device uses an emotion engine to recognize the user's emotional state. Input is the user's operation patterns and voice input (if any), and output is recognized emotion data. Specifically, it analyzes input speed and voice tone to determine whether the user is feeling anxious or stressed.

[0173] Step 7:

[0174] The server adjusts the wording of advice based on the emotional state and provides additional information as needed. The input is emotional data obtained from the emotion engine, and the output is adjusted legal advice and additional information. Specifically, it provides a link to a beginner's guide for users who are showing anxiety.

[0175] (Application Example 2)

[0176] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0177] In reviewing contracts and terms and conditions, traditional systems only assess legal risks, lacking support that considers the user's emotions and stress levels. This makes it difficult for users to confidently understand contract terms and make risk assessments. Furthermore, the lack of appropriate advice tailored to their emotional state results in an inadequate user experience.

[0178] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0179] In this invention, the server includes means for receiving an electronic document using an information processing device and analyzing the electronic document using a natural language processing engine; means for identifying risk elements and generating legal advice based on the analyzed content; means for outputting the generated legal advice to a display device; means for analyzing the user's emotions and adjusting the expression of the legal advice; and means for providing personalized additional information and guidance based on the user's emotional state. This enables the user to accurately understand legal risks in contract review and receive personalized advice that takes their emotions into consideration.

[0180] An "information processing device" is a device that receives electronic documents and has the function of analyzing those documents using a natural language processing engine.

[0181] A "natural language processing engine" is software that takes electronic documents as input data, analyzes their content, and structures it.

[0182] A "risk element" refers to a part of a document, such as a contract, that may contain legal problems or uncertainties.

[0183] "Legal advice" refers to information that provides instructions and advice on legal risks and countermeasures based on the content of the analyzed contract.

[0184] A "display device" is a device used to present generated legal advice to the user.

[0185] "Analyzing user emotions" is the process of inferring a user's emotional state based on their interactions.

[0186] "Personalizing and providing additional information based on emotional state" means selecting and individually presenting appropriate information and support according to the user's emotions.

[0187] This invention is a system that provides risk assessment for electronic documents such as contracts and terms of service, and offers advice tailored to the user's emotions. This system mainly consists of a server, terminals, a natural language processing engine, and an emotion analysis engine.

[0188] Users first upload electronic documents using a device such as a smartphone or computer. During this process, the device identifies the format of the electronic document and converts it to a parsable format as needed. It includes conversion functions that support formats such as PDF and Word files.

[0189] Subsequently, the server analyzes the electronic document using a natural language processing library based on Python (e.g., spaCy or NLTK). It segments the content of the document and identifies risk elements. Furthermore, it refers to legal databases and generates legal advice for the identified risks.

[0190] The generated advice is output to the terminal's display device, and at the same time, the user's interaction is analyzed by an emotion analysis engine. This emotion analysis utilizes machine learning models such as TensorFlow and PyTorch to estimate the user's stress and anxiety levels by evaluating the user's input speed and voice tone.

[0191] Based on the user's emotional state, the generative AI model provides simple and clear advice and offers additional support information. For example, for users who show anxiety, it displays links to FAQs and beginner's guides.

[0192] As a concrete example, consider a scenario where a user uploads a contract related to a new service's pricing plan and wants to know about the risks associated with a specific clause. In this case, the prompt might be, "I want to know what risks are involved in this specific clause of the contract." This system can analyze the relevant clause and directly inform the user, for example, "This clause carries the risk of recurring charges."

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The user selects an electronic document using a terminal and uploads it to the system. In this step, the format of the electronic document (PDF or Word file) is input, and processing is performed to convert it into a parsable format. Specifically, the terminal identifies the file format and converts it to text format as needed. Text data is obtained as output.

[0196] Step 2:

[0197] The server receives the converted text data. A natural language processing engine (e.g., spaCy) analyzes the text data and segments the content of the contract. At this stage, legally important phrases and risk elements are extracted from the input text data. The output is structured data containing the risk elements.

[0198] Step 3:

[0199] The server uses a legal database to generate legal advice based on identified risk factors. Here, risk factors are given as input, and data processing is performed to retrieve corresponding legal information. The output is legal advice to be provided to the user.

[0200] Step 4:

[0201] The server sends the generated legal advice to the terminal's display device. The user can review this, and their interaction at that time leads to the next process. The input is the generated legal advice, and the output is the advice displayed on the user's screen.

[0202] Step 5:

[0203] The emotion analysis engine is activated to analyze the user's interaction data (such as input speed and voice tone). In this step, data calculations are performed to infer the user's emotional state from the input interaction data. The inferred emotional state is obtained as the output.

[0204] Step 6:

[0205] The server utilizes a generative AI model to adjust the wording of advice based on the user's emotional state. Furthermore, it provides additional support information (such as FAQ links) as needed. Input is the result of the emotional analysis and legal advice; output is the adjusted advice and support information.

[0206] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0207] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0208] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0212] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0213] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0214] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0215] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0216] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0217] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0218] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0219] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0220] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0221] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0222] This invention provides a system that allows users to upload contracts via their devices and evaluates the legal risks associated with those contracts using AI technology. The operation of this system's program is described below.

[0223] First, the user operates the terminal to select the contract and upload it to the system. The file formats the user can use are PDF and Word files, which are commonly used and therefore convenient. Then, the user clicks a button to request a review of the contract.

[0224] The server processes electronic documents received from users. First, the server converts the electronic documents into a format that is easy to parse. This process activates a natural language processing engine, which meticulously analyzes the contents of the contract. Specifically, it segments the document by clause, and each segment is tagged. For example, the server identifies confidentiality clauses and limitation of liability clauses and assesses the risks associated with each.

[0225] Subsequently, the AI ​​on the server assesses the legal risks. The AI ​​accesses legal databases and identifies risks for each contract clause, taking into account the latest laws and precedents. This process uses machine learning models pre-trained by experienced legal professionals. If the server determines that the timeframe is inappropriate, it will generate advice such as, "The timeframe in the contract is unclear. We recommend specifying a concrete timeframe."

[0226] Finally, the generated legal advice is sent to the user's device. The user can review the advice on their device and revise the contract as needed. This entire process makes it possible to identify and correct legal deficiencies early. In addition, the system supports global legal requirements and can be used to review contracts in different jurisdictions. In this way, it improves the efficiency of legal operations and enhances risk management.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] Users use their devices to select contract files they want to review and upload them to the system. File formats include PDF and Word.

[0230] Step 2:

[0231] The server receives electronic documents uploaded by users. It then converts the files into a format suitable for text analysis. If the file is a PDF, it uses an open-source PDF conversion library to extract the text.

[0232] Step 3:

[0233] The server activates a natural language processing engine to analyze the contents of the contract. The document is divided into clauses, and clauses such as confidentiality clauses and limitation of liability clauses are tagged. Morphological analysis and sentence parsing techniques are used in this process.

[0234] Step 4:

[0235] The server uses an AI model to evaluate the legal risks in each clause. The AI ​​has previously learned from legal databases and case precedents, and uses this information to scrutinize the contract. If necessary, it performs a risk assessment from a legal perspective.

[0236] Step 5:

[0237] The server automatically generates specific legal advice based on the evaluation results. For example, if the confidentiality clause does not specify a protection period, it will generate advice such as, "It is recommended that the protection period be clearly defined."

[0238] Step 6:

[0239] The generated legal advice is sent from the server to the terminal. The terminal receives it and displays it so that the user can review it.

[0240] Step 7:

[0241] Users can review the advice provided on their device and revise the contract as needed. They can also re-upload the edited content for further analysis.

[0242] (Example 1)

[0243] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0244] There is a challenge in quickly and accurately assessing the legal risks of electronic documents and providing users with appropriate legal advice. In particular, complex legal documents such as contracts have diverse interpretations of clauses, making manual analysis time-consuming and laborious, and making it difficult to reflect the latest legal information. Furthermore, a lack of knowledge of relevant laws and case law can lead to inadequate risk management.

[0245] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0246] This invention includes a server that receives an electronic document, identifies its format, and converts it into an appropriate processing format; a server that uses a natural language processing engine to segment the converted electronic document clause by clause, tags it, and analyzes it; and a server that accesses a legal database and utilizes a machine learning module to identify risk components and generate legal advice based on the analyzed content. This enables the rapid and accurate assessment of legal risks within electronic documents, allowing users to receive timely and accurate legal advice.

[0247] An "information processing device" is a device that has the function of receiving electronic documents, identifying their format, and converting them into an appropriate format.

[0248] A "natural language processing engine" is software that uses computers to analyze human language, structure the text, and understand it.

[0249] A "legal database" is a digital archive in which legal information, including laws and precedents, is organized and stored in a searchable format.

[0250] A "machine learning module" is software that uses algorithms to learn patterns based on data and predict or classify new information.

[0251] "Communication equipment" refers to hardware or software used to send and receive data over a network.

[0252] A "risk component" refers to an element in a contract's clauses or conditions that involves legal risks.

[0253] "Legal advice" refers to suggestions and proposals to minimize risks based on contracts and laws.

[0254] A "user device" is a device operated by the user who ultimately receives the information, and typically includes computers and smartphones.

[0255] This invention relates to a legal document analysis system. Users upload electronic documents, such as contracts, to the system using a terminal. In this process, the terminal receives PDF or Word files provided by the user and sends them to the server.

[0256] The server processes the received electronic documents. First, it identifies the format of the electronic document and converts it into a parsable text format using a document processing tool such as Apache Tika. Next, the server uses a natural language processing engine (e.g., NLTK or spaCy) to segment the text data by clause and tag it according to the characteristics of the legal document.

[0257] Next, the server uses AI to access legal databases and assess the legal risks of the analyzed clauses. Here, a pre-trained machine learning module (for example, a legally specialized BERT model) is used to identify risk elements for each clause and generate legal advice. Based on the AI's assessment, specific advice is generated—for example, "The duration in the contract is unclear. We recommend specifying a concrete duration."

[0258] The generated legal advice is sent to the user's terminal via the server, and the user can review the advice through a display device. This allows the user to review the contents of the contract and make adjustments as needed.

[0259] As a concrete example, when a retailer enters into a new business agreement with an overseas supplier, uploading the contract to a terminal allows the server to detect risks based on local laws and international standards and provide advice. This approach enables companies to smoothly conclude contracts and mitigate risks.

[0260] An example of a prompt for a generative AI model might be, "Evaluate the legal risks of the following contract clause: The contract term is not specified." This would allow the system to identify and suggest specific risks and areas for improvement.

[0261] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0262] Step 1:

[0263] The user selects a contract on the terminal and begins uploading. PDF and Word format electronic documents are selected as input. The terminal then receives the user's input and prepares to send the specified file to the server. The output is the file that has been sent to the server.

[0264] Step 2:

[0265] The server identifies the format of the received electronic document and converts it into a text format that is easy to parse. Specifically, the server uses a document processing tool such as Apache Tika to verify the input file format and then performs data processing to convert it into text data. The output is text data suitable for segmentation and analysis.

[0266] Step 3:

[0267] The server processes the converted text using a natural language processing engine, segmenting and tagging it according to each contract clause. The input is the contract content in text format. The server uses engines such as NLTK and spaCy to extract clauses and perform specific data calculations, such as tagging confidentiality clauses and limitation of liability clauses. The output is data for each tagged clause.

[0268] Step 4:

[0269] The server uses an AI model to assess legal risks based on tagged data. Specifically, it receives tagged clause data as input. The server accesses a legal database and performs data calculations using a trained AI model such as BERT to analyze the risks associated with each clause. This process generates legal advice. The output is a set of risk assessment results and advice based on them.

[0270] Step 5:

[0271] The server sends the generated legal advice to the terminal and displays it to the user. The generated advice data is used as input. The server performs specific data transfer operations, sending the data to the terminal via communication means, allowing the user to view the advice through the display device. The output is legal advice information in a format viewable by the user.

[0272] (Application Example 1)

[0273] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0274] In recent years, there has been a growing need to quickly and accurately assess legal risks in contract document reviews. This process has traditionally relied on manual checks by experts, which is not only time-consuming and laborious but also prone to human error. Furthermore, the need to comply with the differing legal regulations of various jurisdictions in a global business environment adds further complexity. Therefore, there is a demand for a system that can quickly and efficiently handle everything from contract uploads to risk assessments and the provision of legal advice.

[0275] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0276] In this invention, the server includes means for receiving data using an information processing device and analyzing the data using natural language processing means; means for identifying risk factors and generating legal advice based on the analyzed content; means for providing the generated legal advice to an output device; means for providing a function for the user to photograph or transmit the contract using an information terminal; and means for automatically analyzing relevant documents and generating additional information based on the risk assessment results. This makes it possible to efficiently assess the legal risks of a contract and provide appropriate advice quickly, as well as to realize comprehensive support that can respond to a variety of legal situations.

[0277] An "information processing device" is a device that has the ability to receive data and analyze or transform it.

[0278] "Data" refers to various types of electronic information received by information processing devices, and in particular includes electronic documents such as contracts.

[0279] The "natural language processing means" is a technology used to analyze data in the form of natural language, which is a human language, and make its content easier to understand.

[0280] The "risk factor" refers to an element that has potential legal problems or risks in a contract or document.

[0281] The "legal advice" refers to advice or proposals related to the law generated based on risk factors.

[0282] The "output device" is a device used to provide analysis results and legal advice and display them in a form that can be confirmed by the user.

[0283] The "information terminal" refers to an electronic device used by a user to input or operate data.

[0284] "Photographing" is an act of converting a physical document into digital data using a camera, scanner, etc.

[0285] "Transmission" refers to the act of transferring data from one device to another through a communication line.

[0286] The "risk assessment result" refers to the evaluation result of risk factors for the analyzed data and serves as a material for legal judgment.

[0287] The "related document" refers to additional information or documents referred to for complementing or justifying the risk assessment result.

[0288] "Automatically analyze" refers to the process in which a computer system analyzes data without human intervention.

[0289] The "additional information" refers to relevant information or explanations provided to complement the generated legal advice.

[0290] The system that realizes this invention begins with the user uploading a contract. It provides the user with the ability to photograph or send the contract using an information terminal. Smartphones and tablets are commonly used as information terminals.

[0291] The received data is processed on the server by an information processing device. The server analyzes this data using natural language processing. Specifically, it uses the Google Cloud Natural Language API to analyze the clauses within the contract and perform natural language processing to understand their structure. Through this analysis, each segment within the contract is identified, and risk factors are extracted.

[0292] Next, based on the analyzed data, risk factors are evaluated using machine learning methods, and legal advice is generated. In this step, a machine learning model built using TensorFlow identifies high-risk elements by comparing them with a legal database and creates necessary advice. This legal advice is generated as appropriate by a generative AI model.

[0293] The generated legal advice is provided to the user via an output device. The user can view this information on their smartphone or computer screen and make any necessary corrections.

[0294] For example, if a company enters into a sales contract for a new product and the contract term clause is inappropriate, the system will provide legal advice such as, "The contract term is ambiguous. Please specify a concrete term." An example of a prompt to the generating AI model would be, "Identify potentially problematic clauses in the contract and assess the legal risks."

[0295] This system enables users to quickly and efficiently assess the legal risks of contracts, leading to increased efficiency in legal work and improved risk management.

[0296] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0297] Step 1:

[0298] The user uses a terminal to take a picture of the contract or select an existing electronic file and upload it to the system. The input is the captured image data or electronic document, and the output is the data sent to the server.

[0299] Step 2:

[0300] The server checks the format of the received files and converts different formats, such as PDFs, Word files, and image data, into a format that can be parsed. The input is data sent by the user, and the output is text data that is easy to process with natural language processing.

[0301] Step 3:

[0302] The server uses the Google Cloud Natural Language API to analyze text data, identify each clause within the contract, and perform natural language processing. The input is a text-based contract, and the output is a collection of sentences divided into chapters and sections.

[0303] Step 4:

[0304] The server evaluates risk factors using a machine learning model based on the analysis results, employing TensorFlow. The input is the contract details, divided by chapter and section, and the output is a list of identified risk factors.

[0305] Step 5:

[0306] The server generates legal advice based on risk factors. It utilizes a generative AI model to create specific advice. The input is a list of risk factors, and the output is a written legal advice document.

[0307] Step 6:

[0308] The server transmits the legal advice generated to the terminal and provides it to the output device so that the user can view it. The input is the text of the legal advice, and the output is the advice message displayed on the user terminal.

[0309] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0310] This invention is a system in which a user uploads a contract via a terminal, not only evaluates legal risks using AI technology, but also recognizes the user's emotion and adjusts the content of the advice. The operation of the program of this system will be described below.

[0311] The user selects the contract to be reviewed through the terminal and uploads it to the system. The terminal checks the format of the contract file (such as PDF or Word file) and converts it into a format that can be analyzed if necessary. This enables efficient analysis of the contract content by the natural language processing engine.

[0312] The server analyzes the converted electronic document with the natural language processing engine, segments the contract content, and identifies risk elements based on the terms. The AI model generates legal advice regarding the identified risks based on a legal database and case laws. Then, the server transmits this advice to the display device so that the user can view it on the terminal.

[0313] Furthermore, the emotion engine recognizes the emotional state based on the user's interaction. Specifically, it analyzes the context and input speed when the user asks a question, and also the voice tone if there is voice input. Based on this recognition result, the server adjusts the expression of the generated legal advice. For example, when the user is feeling stressed, it responds by providing simple and straightforward advice.

[0314] Furthermore, based on the emotions the emotion engine recognizes, it can personalize and provide relevant additional information and suggestions. For example, if a user expresses anxiety, the server can provide links to frequently asked questions (FAQs) or beginner's guides.

[0315] This system not only allows for proper management of legal risks, but also enables the provision of support tailored to the user's emotional state, resulting in a more personalized user experience.

[0316] The following describes the processing flow.

[0317] Step 1:

[0318] Users use their devices to select contract files to be reviewed and upload them to the system. PDF and Word files are commonly used as contract formats.

[0319] Step 2:

[0320] The terminal sends the uploaded electronic document to the server. The server receives the electronic document and converts it into a format that can be processed. For example, it prepares data for analysis by converting a PDF to text format.

[0321] Step 3:

[0322] The server uses a natural language processing engine to analyze the contract. This analysis divides the document into clauses and identifies confidentiality clauses and limitation of liability clauses. Each segment within the document is tagged, and risks from a legal perspective are extracted.

[0323] Step 4:

[0324] The AI ​​on the server references a legal database and assesses risk factors. Based on pre-trained data, the AI ​​model generates legal advice regarding the risks. For example, if the deadline setting is inappropriate, it might generate advice such as, "We recommend reviewing the protection period settings."

[0325] Step 5:

[0326] The server activates the emotion engine and analyzes the user's emotional state. Based on the user's interactions and input data, it evaluates emotions such as stress and anxiety. If voice input is available, voice tone is also used in the analysis.

[0327] Step 6:

[0328] The server adjusts the generated legal advice to suit the user's emotional state. For example, if the user is expressing anxiety, it prioritizes presenting concise and clear advice.

[0329] Step 7:

[0330] The server sends the adjusted legal advice to the device. The device displays the received advice to the user, who can review it and revise the contract. Personalized suggestions and relevant information based on emotions are also provided.

[0331] (Example 2)

[0332] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0333] Traditional contract analysis systems provide the functionality to legally evaluate the content of documents and identify risks, but they are unable to provide information tailored to the user's emotional state. Therefore, they fail to meet the need to alleviate user anxiety and stress and to offer more personalized advice.

[0334] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0335] In this invention, the server includes means for receiving an electronic document using an information processing device and analyzing the electronic document using a natural language processing engine; means for recognizing the user's emotional state based on user input information and adjusting the expression of legal advice based on the recognized emotional state; and means for providing additional or relevant information according to the user's emotional state. This makes it possible to provide personalized advice tailored to the user's emotional state.

[0336] An "information processing device" is a computer system that receives electronic documents and performs specified processing.

[0337] An "electronic document" is document data composed in a digital format, including file formats such as contracts and reports.

[0338] A "natural language processing engine" is a software technology that analyzes human language and converts it into a format that a computer can understand.

[0339] "Risk elements" refer to parts of an electronic document that are legally important and contain uncertainties or potential problems.

[0340] "Legal advice" refers to guidance and support information provided based on legal standards regarding actions users should take and considerations they should make in specific situations.

[0341] A "display device" is a hardware device, such as a screen or monitor, used to provide information to a user visually.

[0342] "Emotional state" refers to the mental or emotional response a user exhibits at a particular point in time during an interaction.

[0343] "Additional information" refers to supplementary information provided to the user, including data and materials to facilitate a more detailed understanding.

[0344] To implement this invention, the user first selects the contract document they wish to review using a terminal and uploads it electronically to the system. The terminal checks the format of the uploaded document and, if necessary, uses an automatic conversion tool to convert it into a data format that can be properly analyzed, and then sends it to the server as an electronic document. For example, this process includes converting a PDF to a text format.

[0345] The server analyzes received electronic documents using a natural language processing engine. This engine divides the document into segments and identifies the legal elements contained within each segment. An AI model then generates legal advice for the identified risks based on legal databases and past case precedents. This AI model has accumulated a wealth of data on common risks and their countermeasures, enabling sophisticated assessments.

[0346] Once the analysis and advice generation are complete, the server uses an emotion engine to recognize the user's emotional state based on factors such as input speed and tone of voice. Based on this, the server adjusts the wording of the generated legal advice according to the user's emotional state. Furthermore, the server can also provide additional relevant information and legal information to help the user better understand the situation.

[0347] As a concrete example, consider a case where a user uploads a lease agreement. This system would have a server identify important clauses and risks related to the lease agreement (e.g., termination clauses) and provide the user with simple and clear advice. If the server determines that the user is showing anxiety or stress, it would also provide links to beginner-friendly guide information to help them understand with confidence.

[0348] An example of a prompt for using a generative AI model is, "I have uploaded a residential lease agreement. Please assess the risks associated with termination and provide simple advice to users who are feeling stressed." This prompt allows the system to provide assessments and advice tailored to specific user requests.

[0349] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0350] Step 1:

[0351] The user uses a terminal to select the contract they want to review and upload it to the system. The input is the contract file, and the output is the transfer of the file to the system. Specifically, the user selects the contract through a file selection dialog and presses the "Upload" button.

[0352] Step 2:

[0353] The terminal checks the format of the uploaded contract. The input is the file uploaded by the user, and the output is its conversion into a parseable data format. Specifically, if a contract in PDF format is uploaded, the automatic conversion tool converts it to text format and saves it as a string.

[0354] Step 3:

[0355] The server passes the converted electronic document to a natural language processing engine for analysis. The input is contract data converted to text format, and the output is segmented content of the contract. Specifically, the natural language processing engine syntactically analyzes the contract and extracts important keywords and phrases for each clause.

[0356] Step 4:

[0357] The server uses an AI model to assess the legal risks of segmented contract content and generate advice. The input consists of segmented contract content and information from a legal database; the output is legal advice. Specifically, the AI ​​model determines the degree of risk and documents the advice based on relevant case law and regulations.

[0358] Step 5:

[0359] The server sends the generated advice to the terminal, which then displays the advice on its screen. The input is advice data generated by the AI ​​model, and the output is advice information presented visually to the user. Specifically, the terminal displays the advice in a text window so that the user can easily review it.

[0360] Step 6:

[0361] The device uses an emotion engine to recognize the user's emotional state. Input is the user's operation patterns and voice input (if any), and output is recognized emotion data. Specifically, it analyzes input speed and voice tone to determine whether the user is feeling anxious or stressed.

[0362] Step 7:

[0363] The server adjusts the wording of advice based on the emotional state and provides additional information as needed. The input is emotional data obtained from the emotion engine, and the output is adjusted legal advice and additional information. Specifically, it provides a link to a beginner's guide for users who are showing anxiety.

[0364] (Application Example 2)

[0365] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0366] In reviewing contracts and terms and conditions, traditional systems only assess legal risks, lacking support that considers the user's emotions and stress levels. This makes it difficult for users to confidently understand contract terms and make risk assessments. Furthermore, the lack of appropriate advice tailored to their emotional state results in an inadequate user experience.

[0367] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0368] In this invention, the server includes means for receiving an electronic document using an information processing device and analyzing the electronic document using a natural language processing engine; means for identifying risk elements and generating legal advice based on the analyzed content; means for outputting the generated legal advice to a display device; means for analyzing the user's emotions and adjusting the expression of the legal advice; and means for providing personalized additional information and guidance based on the user's emotional state. This enables the user to accurately understand legal risks in contract review and receive personalized advice that takes their emotions into consideration.

[0369] An "information processing device" is a device that receives electronic documents and has the function of analyzing those documents using a natural language processing engine.

[0370] A "natural language processing engine" is software that takes electronic documents as input data, analyzes their content, and structures it.

[0371] A "risk element" refers to a part of a document, such as a contract, that may contain legal problems or uncertainties.

[0372] "Legal advice" refers to information that provides instructions and advice on legal risks and countermeasures based on the content of the analyzed contract.

[0373] A "display device" is a device used to present generated legal advice to the user.

[0374] "Analyzing user emotions" is the process of inferring a user's emotional state based on their interactions.

[0375] "Personalizing and providing additional information based on emotional state" means selecting and individually presenting appropriate information and support according to the user's emotions.

[0376] This invention is a system that provides risk assessment for electronic documents such as contracts and terms of service, and offers advice tailored to the user's emotions. This system mainly consists of a server, terminals, a natural language processing engine, and an emotion analysis engine.

[0377] Users first upload electronic documents using a device such as a smartphone or computer. During this process, the device identifies the format of the electronic document and converts it to a parsable format as needed. It includes conversion functions that support formats such as PDF and Word files.

[0378] Subsequently, the server analyzes the electronic document using a natural language processing library based on Python (e.g., spaCy or NLTK). It segments the content of the document and identifies risk elements. Furthermore, it refers to legal databases and generates legal advice for the identified risks.

[0379] The generated advice is output to the terminal's display device, and at the same time, the user's interaction is analyzed by an emotion analysis engine. This emotion analysis utilizes machine learning models such as TensorFlow and PyTorch to estimate the user's stress and anxiety levels by evaluating the user's input speed and voice tone.

[0380] Based on the user's emotional state, the generative AI model provides simple and clear advice and offers additional support information. For example, for users who show anxiety, it displays links to FAQs and beginner's guides.

[0381] As a concrete example, consider a scenario where a user uploads a contract related to a new service's pricing plan and wants to know about the risks associated with a specific clause. In this case, the prompt might be, "I want to know what risks are involved in this specific clause of the contract." This system can analyze the relevant clause and directly inform the user, for example, "This clause carries the risk of recurring charges."

[0382] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0383] Step 1:

[0384] The user selects an electronic document using a terminal and uploads it to the system. In this step, the format of the electronic document (PDF or Word file) is input, and processing is performed to convert it into a parsable format. Specifically, the terminal identifies the file format and converts it to text format as needed. Text data is obtained as output.

[0385] Step 2:

[0386] The server receives the converted text data. A natural language processing engine (e.g., spaCy) analyzes the text data and segments the content of the contract. At this stage, legally important phrases and risk elements are extracted from the input text data. The output is structured data containing the risk elements.

[0387] Step 3:

[0388] The server uses a legal database to generate legal advice based on identified risk factors. Here, risk factors are given as input, and data processing is performed to retrieve corresponding legal information. The output is legal advice to be provided to the user.

[0389] Step 4:

[0390] The server sends the generated legal advice to the terminal's display device. The user can review this, and their interaction at that time leads to the next process. The input is the generated legal advice, and the output is the advice displayed on the user's screen.

[0391] Step 5:

[0392] The emotion analysis engine is activated to analyze the user's interaction data (such as input speed and voice tone). In this step, data calculations are performed to infer the user's emotional state from the input interaction data. The inferred emotional state is obtained as the output.

[0393] Step 6:

[0394] The server utilizes a generative AI model to adjust the wording of advice based on the user's emotional state. Furthermore, it provides additional support information (such as FAQ links) as needed. Input is the result of the emotional analysis and legal advice; output is the adjusted advice and support information.

[0395] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0396] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0397] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0398] [Third Embodiment]

[0399] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0400] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0401] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0402] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0403] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0404] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0405] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0406] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0407] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0408] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0409] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0410] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0411] This invention provides a system that allows users to upload contracts via their devices and evaluates the legal risks associated with those contracts using AI technology. The operation of this system's program is described below.

[0412] First, the user operates the terminal to select the contract and upload it to the system. The file formats the user can use are PDF and Word files, which are commonly used and therefore convenient. Then, the user clicks a button to request a review of the contract.

[0413] The server processes electronic documents received from users. First, the server converts the electronic documents into a format that is easy to parse. This process activates a natural language processing engine, which meticulously analyzes the contents of the contract. Specifically, it segments the document by clause, and each segment is tagged. For example, the server identifies confidentiality clauses and limitation of liability clauses and assesses the risks associated with each.

[0414] Subsequently, the AI ​​on the server assesses the legal risks. The AI ​​accesses legal databases and identifies risks for each contract clause, taking into account the latest laws and precedents. This process uses machine learning models pre-trained by experienced legal professionals. If the server determines that the timeframe is inappropriate, it will generate advice such as, "The timeframe in the contract is unclear. We recommend specifying a concrete timeframe."

[0415] Finally, the generated legal advice is sent to the user's device. The user can review the advice on their device and revise the contract as needed. This entire process makes it possible to identify and correct legal deficiencies early. In addition, the system supports global legal requirements and can be used to review contracts in different jurisdictions. In this way, it improves the efficiency of legal operations and enhances risk management.

[0416] The following describes the processing flow.

[0417] Step 1:

[0418] Users use their devices to select contract files they want to review and upload them to the system. File formats include PDF and Word.

[0419] Step 2:

[0420] The server receives electronic documents uploaded by users. It then converts the files into a format suitable for text analysis. If the file is a PDF, it uses an open-source PDF conversion library to extract the text.

[0421] Step 3:

[0422] The server activates a natural language processing engine to analyze the contents of the contract. The document is divided into clauses, and clauses such as confidentiality clauses and limitation of liability clauses are tagged. Morphological analysis and sentence parsing techniques are used in this process.

[0423] Step 4:

[0424] The server uses an AI model to evaluate the legal risks in each clause. The AI ​​has previously learned from legal databases and case precedents, and uses this information to scrutinize the contract. If necessary, it performs a risk assessment from a legal perspective.

[0425] Step 5:

[0426] The server automatically generates specific legal advice based on the evaluation results. For example, if the confidentiality clause does not specify a protection period, it will generate advice such as, "It is recommended that the protection period be clearly defined."

[0427] Step 6:

[0428] The generated legal advice is sent from the server to the terminal. The terminal receives it and displays it so that the user can review it.

[0429] Step 7:

[0430] Users can review the advice provided on their device and revise the contract as needed. They can also re-upload the edited content for further analysis.

[0431] (Example 1)

[0432] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0433] There is a challenge in quickly and accurately assessing the legal risks of electronic documents and providing users with appropriate legal advice. In particular, complex legal documents such as contracts have diverse interpretations of clauses, making manual analysis time-consuming and laborious, and making it difficult to reflect the latest legal information. Furthermore, a lack of knowledge of relevant laws and case law can lead to inadequate risk management.

[0434] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0435] This invention includes a server that receives an electronic document, identifies its format, and converts it into an appropriate processing format; a server that uses a natural language processing engine to segment the converted electronic document clause by clause, tags it, and analyzes it; and a server that accesses a legal database and utilizes a machine learning module to identify risk components and generate legal advice based on the analyzed content. This enables the rapid and accurate assessment of legal risks within electronic documents, allowing users to receive timely and accurate legal advice.

[0436] An "information processing device" is a device that has the function of receiving electronic documents, identifying their format, and converting them into an appropriate format.

[0437] A "natural language processing engine" is software that uses computers to analyze human language, structure the text, and understand it.

[0438] A "legal database" is a digital archive in which legal information, including laws and precedents, is organized and stored in a searchable format.

[0439] A "machine learning module" is software that uses algorithms to learn patterns based on data and predict or classify new information.

[0440] "Communication equipment" refers to hardware or software used to send and receive data over a network.

[0441] A "risk component" refers to an element in a contract's clauses or conditions that involves legal risks.

[0442] "Legal advice" refers to suggestions and proposals to minimize risks based on contracts and laws.

[0443] A "user device" is a device operated by the user who ultimately receives the information, and typically includes computers and smartphones.

[0444] This invention relates to a legal document analysis system. Users upload electronic documents, such as contracts, to the system using a terminal. In this process, the terminal receives PDF or Word files provided by the user and sends them to the server.

[0445] The server processes the received electronic documents. First, it identifies the format of the electronic document and converts it into a parsable text format using a document processing tool such as Apache Tika. Next, the server uses a natural language processing engine (e.g., NLTK or spaCy) to segment the text data by clause and tag it according to the characteristics of the legal document.

[0446] Next, the server uses AI to access legal databases and assess the legal risks of the analyzed clauses. Here, a pre-trained machine learning module (for example, a legally specialized BERT model) is used to identify risk elements for each clause and generate legal advice. Based on the AI's assessment, specific advice is generated—for example, "The duration in the contract is unclear. We recommend specifying a concrete duration."

[0447] The generated legal advice is sent to the user's terminal via the server, and the user can review the advice through a display device. This allows the user to review the contents of the contract and make adjustments as needed.

[0448] As a concrete example, when a retailer enters into a new business agreement with an overseas supplier, uploading the contract to a terminal allows the server to detect risks based on local laws and international standards and provide advice. This approach enables companies to smoothly conclude contracts and mitigate risks.

[0449] An example of a prompt for a generative AI model might be, "Evaluate the legal risks of the following contract clause: The contract term is not specified." This would allow the system to identify and suggest specific risks and areas for improvement.

[0450] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0451] Step 1:

[0452] The user selects a contract on the terminal and begins uploading. PDF and Word format electronic documents are selected as input. The terminal then receives the user's input and prepares to send the specified file to the server. The output is the file that has been sent to the server.

[0453] Step 2:

[0454] The server identifies the format of the received electronic document and converts it into a text format that is easy to parse. Specifically, the server uses a document processing tool such as Apache Tika to verify the input file format and then performs data processing to convert it into text data. The output is text data suitable for segmentation and analysis.

[0455] Step 3:

[0456] The server processes the converted text using a natural language processing engine, segmenting and tagging it according to each contract clause. The input is the contract content in text format. The server uses engines such as NLTK and spaCy to extract clauses and perform specific data calculations, such as tagging confidentiality clauses and limitation of liability clauses. The output is data for each tagged clause.

[0457] Step 4:

[0458] The server uses an AI model to assess legal risks based on tagged data. Specifically, it receives tagged clause data as input. The server accesses a legal database and performs data calculations using a trained AI model such as BERT to analyze the risks associated with each clause. This process generates legal advice. The output is a set of risk assessment results and advice based on them.

[0459] Step 5:

[0460] The server sends the generated legal advice to the terminal and displays it to the user. The generated advice data is used as input. The server performs specific data transfer operations, sending the data to the terminal via communication means, allowing the user to view the advice through the display device. The output is legal advice information in a format viewable by the user.

[0461] (Application Example 1)

[0462] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0463] In recent years, there has been a growing need to quickly and accurately assess legal risks in contract document reviews. This process has traditionally relied on manual checks by experts, which is not only time-consuming and laborious but also prone to human error. Furthermore, the need to comply with the differing legal regulations of various jurisdictions in a global business environment adds further complexity. Therefore, there is a demand for a system that can quickly and efficiently handle everything from contract uploads to risk assessments and the provision of legal advice.

[0464] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0465] In this invention, the server includes means for receiving data using an information processing device and analyzing the data using natural language processing means; means for identifying risk factors and generating legal advice based on the analyzed content; means for providing the generated legal advice to an output device; means for providing a function for the user to photograph or transmit the contract using an information terminal; and means for automatically analyzing relevant documents and generating additional information based on the risk assessment results. This makes it possible to efficiently assess the legal risks of a contract and provide appropriate advice quickly, as well as to realize comprehensive support that can respond to a variety of legal situations.

[0466] An "information processing device" is a device that has the ability to receive data and analyze or transform it.

[0467] "Data" refers to various types of electronic information received by information processing devices, and in particular includes electronic documents such as contracts.

[0468] "Natural language processing" refers to technologies used to analyze data in the form of natural language, which is human language, in order to make its content easier to understand.

[0469] A "risk factor" refers to an element within a contract or document that may contain potential legal issues or risks.

[0470] "Legal advice" refers to legal advice or suggestions that are generated based on risk factors.

[0471] An "output device" is a device used to provide analysis results and legal advice, and to display them in a format that can be reviewed by the user.

[0472] An "information terminal" refers to an electronic device used by a user to input or manipulate data.

[0473] "Photography" is the act of converting a physical document into digital data using a camera, scanner, or other device.

[0474] "Transmission" refers to the act of transferring data from one device to another via a communication line.

[0475] "Risk assessment results" refer to the evaluation results of risk factors in the analyzed data, and serve as legal basis for judgments.

[0476] "Related documents" refer to additional information or documents that are referenced to complement or support the risk assessment results.

[0477] "Automatic analysis" refers to the process by which a computer system analyzes data without human intervention.

[0478] "Additional information" refers to relevant information and explanations provided to complement the generated legal advice.

[0479] The system that realizes this invention begins with the user uploading a contract. It provides the user with the ability to photograph or send the contract using an information terminal. Smartphones and tablets are commonly used as information terminals.

[0480] The received data is processed on the server by an information processing device. The server analyzes this data using natural language processing. Specifically, it uses the Google Cloud Natural Language API to analyze the clauses within the contract and perform natural language processing to understand their structure. Through this analysis, each segment within the contract is identified, and risk factors are extracted.

[0481] Next, based on the analyzed data, risk factors are evaluated using machine learning methods, and legal advice is generated. In this step, a machine learning model built using TensorFlow identifies high-risk elements by comparing them with a legal database and creates necessary advice. This legal advice is generated as appropriate by a generative AI model.

[0482] The generated legal advice is provided to the user via an output device. The user can view this information on their smartphone or computer screen and make any necessary corrections.

[0483] For example, if a company enters into a sales contract for a new product and the contract term clause is inappropriate, the system will provide legal advice such as, "The contract term is ambiguous. Please specify a concrete term." An example of a prompt to the generating AI model would be, "Identify potentially problematic clauses in the contract and assess the legal risks."

[0484] This system enables users to quickly and efficiently assess the legal risks of contracts, leading to increased efficiency in legal work and improved risk management.

[0485] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0486] Step 1:

[0487] The user uses a terminal to take a picture of the contract or select an existing electronic file and upload it to the system. The input is the captured image data or electronic document, and the output is the data sent to the server.

[0488] Step 2:

[0489] The server checks the format of the received files and converts different formats, such as PDFs, Word files, and image data, into a format that can be parsed. The input is data sent by the user, and the output is text data that is easy to process with natural language processing.

[0490] Step 3:

[0491] The server uses the Google Cloud Natural Language API to analyze text data, identify each clause within the contract, and perform natural language processing. The input is a text-based contract, and the output is a collection of sentences divided into chapters and sections.

[0492] Step 4:

[0493] The server evaluates risk factors using a machine learning model based on the analysis results, employing TensorFlow. The input is the contract details, divided by chapter and section, and the output is a list of identified risk factors.

[0494] Step 5:

[0495] The server generates legal advice based on risk factors. It utilizes a generative AI model to create specific advice. The input is a list of risk factors, and the output is a written legal advice document.

[0496] Step 6:

[0497] The server generates legal advice and sends it to the terminal, providing it to the output device for user review. The input is the legal advice text, and the output is the advice message displayed on the user's terminal.

[0498] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0499] This invention is a system in which a user uploads a contract via a terminal, and AI technology is used not only to assess the legal risks but also to recognize the user's emotions and adjust the advice accordingly. The operation of this system's program is described below.

[0500] Users select the contract they want to review via their device and upload it to the system. The device checks the format of the contract file (such as PDF or Word) and converts it to a format suitable for analysis as needed. This enables efficient analysis of the contract content using a natural language processing engine.

[0501] The server analyzes the converted electronic document using a natural language processing engine, segmenting the contract content and identifying risk elements based on the clauses. The AI ​​model generates legal advice regarding the identified risks based on legal databases and case law. The server then sends this advice to a display device so that the user can view it on their device.

[0502] Furthermore, the emotion engine recognizes the user's emotional state based on their interaction with the system. Specifically, it analyzes the context and typing speed of the user's questions, and, if voice input is available, the tone of their voice. Based on these recognition results, the server adjusts the wording of the generated legal advice. For example, if the user is feeling stressed, the system will provide simple and clear advice.

[0503] Furthermore, based on the emotions the emotion engine recognizes, it can personalize and provide relevant additional information and suggestions. For example, if a user expresses anxiety, the server can provide links to frequently asked questions (FAQs) or beginner's guides.

[0504] This system not only allows for proper management of legal risks, but also enables the provision of support tailored to the user's emotional state, resulting in a more personalized user experience.

[0505] The following describes the processing flow.

[0506] Step 1:

[0507] Users use their devices to select contract files to be reviewed and upload them to the system. PDF and Word files are commonly used as contract formats.

[0508] Step 2:

[0509] The terminal sends the uploaded electronic document to the server. The server receives the electronic document and converts it into a format that can be processed. For example, it prepares data for analysis by converting a PDF to text format.

[0510] Step 3:

[0511] The server uses a natural language processing engine to analyze the contract. This analysis divides the document into clauses and identifies confidentiality clauses and limitation of liability clauses. Each segment within the document is tagged, and risks from a legal perspective are extracted.

[0512] Step 4:

[0513] The AI ​​on the server references a legal database and assesses risk factors. Based on pre-trained data, the AI ​​model generates legal advice regarding the risks. For example, if the deadline setting is inappropriate, it might generate advice such as, "We recommend reviewing the protection period settings."

[0514] Step 5:

[0515] The server activates the emotion engine and analyzes the user's emotional state. Based on the user's interactions and input data, it evaluates emotions such as stress and anxiety. If voice input is available, voice tone is also used in the analysis.

[0516] Step 6:

[0517] The server adjusts the generated legal advice to suit the user's emotional state. For example, if the user is expressing anxiety, it prioritizes presenting concise and clear advice.

[0518] Step 7:

[0519] The server sends the adjusted legal advice to the device. The device displays the received advice to the user, who can review it and revise the contract. Personalized suggestions and relevant information based on emotions are also provided.

[0520] (Example 2)

[0521] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0522] Traditional contract analysis systems provide the functionality to legally evaluate the content of documents and identify risks, but they are unable to provide information tailored to the user's emotional state. Therefore, they fail to meet the need to alleviate user anxiety and stress and to offer more personalized advice.

[0523] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0524] In this invention, the server includes means for receiving an electronic document using an information processing device and analyzing the electronic document using a natural language processing engine; means for recognizing the user's emotional state based on user input information and adjusting the expression of legal advice based on the recognized emotional state; and means for providing additional or relevant information according to the user's emotional state. This makes it possible to provide personalized advice tailored to the user's emotional state.

[0525] An "information processing device" is a computer system that receives electronic documents and performs specified processing.

[0526] An "electronic document" is document data composed in a digital format, including file formats such as contracts and reports.

[0527] A "natural language processing engine" is a software technology that analyzes human language and converts it into a format that a computer can understand.

[0528] "Risk elements" refer to parts of an electronic document that are legally important and contain uncertainties or potential problems.

[0529] "Legal advice" refers to guidance and support information provided based on legal standards regarding actions users should take and considerations they should make in specific situations.

[0530] A "display device" is a hardware device, such as a screen or monitor, used to provide information to a user visually.

[0531] "Emotional state" refers to the mental or emotional response a user exhibits at a particular point in time during an interaction.

[0532] "Additional information" refers to supplementary information provided to the user, including data and materials to facilitate a more detailed understanding.

[0533] To implement this invention, the user first selects the contract document they wish to review using a terminal and uploads it electronically to the system. The terminal checks the format of the uploaded document and, if necessary, uses an automatic conversion tool to convert it into a data format that can be properly analyzed, and then sends it to the server as an electronic document. For example, this process includes converting a PDF to a text format.

[0534] The server analyzes received electronic documents using a natural language processing engine. This engine divides the document into segments and identifies the legal elements contained within each segment. An AI model then generates legal advice for the identified risks based on legal databases and past case precedents. This AI model has accumulated a wealth of data on common risks and their countermeasures, enabling sophisticated assessments.

[0535] Once the analysis and advice generation are complete, the server uses an emotion engine to recognize the user's emotional state based on factors such as input speed and tone of voice. Based on this, the server adjusts the wording of the generated legal advice according to the user's emotional state. Furthermore, the server can also provide additional relevant information and legal information to help the user better understand the situation.

[0536] As a concrete example, consider a case where a user uploads a lease agreement. This system would have a server identify important clauses and risks related to the lease agreement (e.g., termination clauses) and provide the user with simple and clear advice. If the server determines that the user is showing anxiety or stress, it would also provide links to beginner-friendly guide information to help them understand with confidence.

[0537] An example of a prompt for using a generative AI model is, "I have uploaded a residential lease agreement. Please assess the risks associated with termination and provide simple advice to users who are feeling stressed." This prompt allows the system to provide assessments and advice tailored to specific user requests.

[0538] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0539] Step 1:

[0540] The user uses a terminal to select the contract they want to review and upload it to the system. The input is the contract file, and the output is the transfer of the file to the system. Specifically, the user selects the contract through a file selection dialog and presses the "Upload" button.

[0541] Step 2:

[0542] The terminal checks the format of the uploaded contract. The input is the file uploaded by the user, and the output is its conversion into a parseable data format. Specifically, if a contract in PDF format is uploaded, the automatic conversion tool converts it to text format and saves it as a string.

[0543] Step 3:

[0544] The server passes the converted electronic document to a natural language processing engine for analysis. The input is contract data converted to text format, and the output is segmented content of the contract. Specifically, the natural language processing engine syntactically analyzes the contract and extracts important keywords and phrases for each clause.

[0545] Step 4:

[0546] The server uses an AI model to assess the legal risks of segmented contract content and generate advice. The input consists of segmented contract content and information from a legal database; the output is legal advice. Specifically, the AI ​​model determines the degree of risk and documents the advice based on relevant case law and regulations.

[0547] Step 5:

[0548] The server sends the generated advice to the terminal, which then displays the advice on its screen. The input is advice data generated by the AI ​​model, and the output is advice information presented visually to the user. Specifically, the terminal displays the advice in a text window so that the user can easily review it.

[0549] Step 6:

[0550] The device uses an emotion engine to recognize the user's emotional state. Input is the user's operation patterns and voice input (if any), and output is recognized emotion data. Specifically, it analyzes input speed and voice tone to determine whether the user is feeling anxious or stressed.

[0551] Step 7:

[0552] The server adjusts the wording of advice based on the emotional state and provides additional information as needed. The input is emotional data obtained from the emotion engine, and the output is adjusted legal advice and additional information. Specifically, it provides a link to a beginner's guide for users who are showing anxiety.

[0553] (Application Example 2)

[0554] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0555] In reviewing contracts and terms and conditions, traditional systems only assess legal risks, lacking support that considers the user's emotions and stress levels. This makes it difficult for users to confidently understand contract terms and make risk assessments. Furthermore, the lack of appropriate advice tailored to their emotional state results in an inadequate user experience.

[0556] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0557] In this invention, the server includes means for receiving an electronic document using an information processing device and analyzing the electronic document using a natural language processing engine; means for identifying risk elements and generating legal advice based on the analyzed content; means for outputting the generated legal advice to a display device; means for analyzing the user's emotions and adjusting the expression of the legal advice; and means for providing personalized additional information and guidance based on the user's emotional state. This enables the user to accurately understand legal risks in contract review and receive personalized advice that takes their emotions into consideration.

[0558] An "information processing device" is a device that receives electronic documents and has the function of analyzing those documents using a natural language processing engine.

[0559] A "natural language processing engine" is software that takes electronic documents as input data, analyzes their content, and structures it.

[0560] A "risk element" refers to a part of a document, such as a contract, that may contain legal problems or uncertainties.

[0561] "Legal advice" refers to information that provides instructions and advice on legal risks and countermeasures based on the content of the analyzed contract.

[0562] A "display device" is a device used to present generated legal advice to the user.

[0563] "Analyzing user emotions" is the process of inferring a user's emotional state based on their interactions.

[0564] "Personalizing and providing additional information based on emotional state" means selecting and individually presenting appropriate information and support according to the user's emotions.

[0565] This invention is a system that provides risk assessment for electronic documents such as contracts and terms of service, and offers advice tailored to the user's emotions. This system mainly consists of a server, terminals, a natural language processing engine, and an emotion analysis engine.

[0566] Users first upload electronic documents using a device such as a smartphone or computer. During this process, the device identifies the format of the electronic document and converts it to a parsable format as needed. It includes conversion functions that support formats such as PDF and Word files.

[0567] Subsequently, the server analyzes the electronic document using a natural language processing library based on Python (e.g., spaCy or NLTK). It segments the content of the document and identifies risk elements. Furthermore, it refers to legal databases and generates legal advice for the identified risks.

[0568] The generated advice is output to the terminal's display device, and at the same time, the user's interaction is analyzed by an emotion analysis engine. This emotion analysis utilizes machine learning models such as TensorFlow and PyTorch to estimate the user's stress and anxiety levels by evaluating the user's input speed and voice tone.

[0569] Based on the user's emotional state, the generative AI model provides simple and clear advice and offers additional support information. For example, for users who show anxiety, it displays links to FAQs and beginner's guides.

[0570] As a concrete example, consider a scenario where a user uploads a contract related to a new service's pricing plan and wants to know about the risks associated with a specific clause. In this case, the prompt might be, "I want to know what risks are involved in this specific clause of the contract." This system can analyze the relevant clause and directly inform the user, for example, "This clause carries the risk of recurring charges."

[0571] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0572] Step 1:

[0573] The user selects an electronic document using a terminal and uploads it to the system. In this step, the format of the electronic document (PDF or Word file) is input, and processing is performed to convert it into a parsable format. Specifically, the terminal identifies the file format and converts it to text format as needed. Text data is obtained as output.

[0574] Step 2:

[0575] The server receives the converted text data. A natural language processing engine (e.g., spaCy) analyzes the text data and segments the content of the contract. At this stage, legally important phrases and risk elements are extracted from the input text data. The output is structured data containing the risk elements.

[0576] Step 3:

[0577] The server uses a legal database to generate legal advice based on identified risk factors. Here, risk factors are given as input, and data processing is performed to retrieve corresponding legal information. The output is legal advice to be provided to the user.

[0578] Step 4:

[0579] The server sends the generated legal advice to the terminal's display device. The user can review this, and their interaction at that time leads to the next process. The input is the generated legal advice, and the output is the advice displayed on the user's screen.

[0580] Step 5:

[0581] The emotion analysis engine is activated to analyze the user's interaction data (such as input speed and voice tone). In this step, data calculations are performed to infer the user's emotional state from the input interaction data. The inferred emotional state is obtained as the output.

[0582] Step 6:

[0583] The server utilizes a generative AI model to adjust the wording of advice based on the user's emotional state. Furthermore, it provides additional support information (such as FAQ links) as needed. Input is the result of the emotional analysis and legal advice; output is the adjusted advice and support information.

[0584] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0585] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0586] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0587] [Fourth Embodiment]

[0588] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0589] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0590] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0591] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0592] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0593] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0594] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0595] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0596] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0597] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0598] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0599] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0600] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0601] This invention provides a system that allows users to upload contracts via their devices and evaluates the legal risks associated with those contracts using AI technology. The operation of this system's program is described below.

[0602] First, the user operates the terminal to select the contract and upload it to the system. The file formats the user can use are PDF and Word files, which are commonly used and therefore convenient. Then, the user clicks a button to request a review of the contract.

[0603] The server processes electronic documents received from users. First, the server converts the electronic documents into a format that is easy to parse. This process activates a natural language processing engine, which meticulously analyzes the contents of the contract. Specifically, it segments the document by clause, and each segment is tagged. For example, the server identifies confidentiality clauses and limitation of liability clauses and assesses the risks associated with each.

[0604] Subsequently, the AI ​​on the server assesses the legal risks. The AI ​​accesses legal databases and identifies risks for each contract clause, taking into account the latest laws and precedents. This process uses machine learning models pre-trained by experienced legal professionals. If the server determines that the timeframe is inappropriate, it will generate advice such as, "The timeframe in the contract is unclear. We recommend specifying a concrete timeframe."

[0605] Finally, the generated legal advice is sent to the user's device. The user can review the advice on their device and revise the contract as needed. This entire process makes it possible to identify and correct legal deficiencies early. In addition, the system supports global legal requirements and can be used to review contracts in different jurisdictions. In this way, it improves the efficiency of legal operations and enhances risk management.

[0606] The following describes the processing flow.

[0607] Step 1:

[0608] Users use their devices to select contract files they want to review and upload them to the system. File formats include PDF and Word.

[0609] Step 2:

[0610] The server receives electronic documents uploaded by users. It then converts the files into a format suitable for text analysis. If the file is a PDF, it uses an open-source PDF conversion library to extract the text.

[0611] Step 3:

[0612] The server activates a natural language processing engine to analyze the contents of the contract. The document is divided into clauses, and clauses such as confidentiality clauses and limitation of liability clauses are tagged. Morphological analysis and sentence parsing techniques are used in this process.

[0613] Step 4:

[0614] The server uses an AI model to evaluate the legal risks in each clause. The AI ​​has previously learned from legal databases and case precedents, and uses this information to scrutinize the contract. If necessary, it performs a risk assessment from a legal perspective.

[0615] Step 5:

[0616] The server automatically generates specific legal advice based on the evaluation results. For example, if the confidentiality clause does not specify a protection period, it will generate advice such as, "It is recommended that the protection period be clearly defined."

[0617] Step 6:

[0618] The generated legal advice is sent from the server to the terminal. The terminal receives it and displays it so that the user can review it.

[0619] Step 7:

[0620] Users can review the advice provided on their device and revise the contract as needed. They can also re-upload the edited content for further analysis.

[0621] (Example 1)

[0622] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0623] There is a challenge in quickly and accurately assessing the legal risks of electronic documents and providing users with appropriate legal advice. In particular, complex legal documents such as contracts have diverse interpretations of clauses, making manual analysis time-consuming and laborious, and making it difficult to reflect the latest legal information. Furthermore, a lack of knowledge of relevant laws and case law can lead to inadequate risk management.

[0624] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0625] This invention includes a server that receives an electronic document, identifies its format, and converts it into an appropriate processing format; a server that uses a natural language processing engine to segment the converted electronic document clause by clause, tags it, and analyzes it; and a server that accesses a legal database and utilizes a machine learning module to identify risk components and generate legal advice based on the analyzed content. This enables the rapid and accurate assessment of legal risks within electronic documents, allowing users to receive timely and accurate legal advice.

[0626] An "information processing device" is a device that has the function of receiving electronic documents, identifying their format, and converting them into an appropriate format.

[0627] A "natural language processing engine" is software that uses computers to analyze human language, structure the text, and understand it.

[0628] A "legal database" is a digital archive in which legal information, including laws and precedents, is organized and stored in a searchable format.

[0629] A "machine learning module" is software that uses algorithms to learn patterns based on data and predict or classify new information.

[0630] "Communication equipment" refers to hardware or software used to send and receive data over a network.

[0631] A "risk component" refers to an element in a contract's clauses or conditions that involves legal risks.

[0632] "Legal advice" refers to suggestions and proposals to minimize risks based on contracts and laws.

[0633] A "user device" is a device operated by the user who ultimately receives the information, and typically includes computers and smartphones.

[0634] This invention relates to a legal document analysis system. Users upload electronic documents, such as contracts, to the system using a terminal. In this process, the terminal receives PDF or Word files provided by the user and sends them to the server.

[0635] The server processes the received electronic documents. First, it identifies the format of the electronic document and converts it into a parsable text format using a document processing tool such as Apache Tika. Next, the server uses a natural language processing engine (e.g., NLTK or spaCy) to segment the text data by clause and tag it according to the characteristics of the legal document.

[0636] Next, the server uses AI to access legal databases and assess the legal risks of the analyzed clauses. Here, a pre-trained machine learning module (for example, a legally specialized BERT model) is used to identify risk elements for each clause and generate legal advice. Based on the AI's assessment, specific advice is generated—for example, "The duration in the contract is unclear. We recommend specifying a concrete duration."

[0637] The generated legal advice is sent to the user's terminal via the server, and the user can review the advice through a display device. This allows the user to review the contents of the contract and make adjustments as needed.

[0638] As a concrete example, when a retailer enters into a new business agreement with an overseas supplier, uploading the contract to a terminal allows the server to detect risks based on local laws and international standards and provide advice. This approach enables companies to smoothly conclude contracts and mitigate risks.

[0639] An example of a prompt for a generative AI model might be, "Evaluate the legal risks of the following contract clause: The contract term is not specified." This would allow the system to identify and suggest specific risks and areas for improvement.

[0640] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0641] Step 1:

[0642] The user selects a contract on the terminal and begins uploading. PDF and Word format electronic documents are selected as input. The terminal then receives the user's input and prepares to send the specified file to the server. The output is the file that has been sent to the server.

[0643] Step 2:

[0644] The server identifies the format of the received electronic document and converts it into a text format that is easy to parse. Specifically, the server uses a document processing tool such as Apache Tika to verify the input file format and then performs data processing to convert it into text data. The output is text data suitable for segmentation and analysis.

[0645] Step 3:

[0646] The server processes the converted text using a natural language processing engine, segmenting and tagging it according to each contract clause. The input is the contract content in text format. The server uses engines such as NLTK and spaCy to extract clauses and perform specific data calculations, such as tagging confidentiality clauses and limitation of liability clauses. The output is data for each tagged clause.

[0647] Step 4:

[0648] The server uses an AI model to assess legal risks based on tagged data. Specifically, it receives tagged clause data as input. The server accesses a legal database and performs data calculations using a trained AI model such as BERT to analyze the risks associated with each clause. This process generates legal advice. The output is a set of risk assessment results and advice based on them.

[0649] Step 5:

[0650] The server sends the generated legal advice to the terminal and displays it to the user. The generated advice data is used as input. The server performs specific data transfer operations, sending the data to the terminal via communication means, allowing the user to view the advice through the display device. The output is legal advice information in a format viewable by the user.

[0651] (Application Example 1)

[0652] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0653] In recent years, there has been a growing need to quickly and accurately assess legal risks in contract document reviews. This process has traditionally relied on manual checks by experts, which is not only time-consuming and laborious but also prone to human error. Furthermore, the need to comply with the differing legal regulations of various jurisdictions in a global business environment adds further complexity. Therefore, there is a demand for a system that can quickly and efficiently handle everything from contract uploads to risk assessments and the provision of legal advice.

[0654] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0655] In this invention, the server includes means for receiving data using an information processing device and analyzing the data using natural language processing means; means for identifying risk factors and generating legal advice based on the analyzed content; means for providing the generated legal advice to an output device; means for providing a function for the user to photograph or transmit the contract using an information terminal; and means for automatically analyzing relevant documents and generating additional information based on the risk assessment results. This makes it possible to efficiently assess the legal risks of a contract and provide appropriate advice quickly, as well as to realize comprehensive support that can respond to a variety of legal situations.

[0656] An "information processing device" is a device that has the ability to receive data and analyze or transform it.

[0657] "Data" refers to various types of electronic information received by information processing devices, and in particular includes electronic documents such as contracts.

[0658] "Natural language processing" refers to technologies used to analyze data in the form of natural language, which is human language, in order to make its content easier to understand.

[0659] A "risk factor" refers to an element within a contract or document that may contain potential legal issues or risks.

[0660] "Legal advice" refers to legal advice or suggestions that are generated based on risk factors.

[0661] An "output device" is a device used to provide analysis results and legal advice, and to display them in a format that can be reviewed by the user.

[0662] An "information terminal" refers to an electronic device used by a user to input or manipulate data.

[0663] "Photography" is the act of converting a physical document into digital data using a camera, scanner, or other device.

[0664] "Transmission" refers to the act of transferring data from one device to another via a communication line.

[0665] "Risk assessment results" refer to the evaluation results of risk factors in the analyzed data, and serve as legal basis for judgments.

[0666] "Related documents" refer to additional information or documents that are referenced to complement or support the risk assessment results.

[0667] "Automatic analysis" refers to the process by which a computer system analyzes data without human intervention.

[0668] "Additional information" refers to relevant information and explanations provided to complement the generated legal advice.

[0669] The system that realizes this invention begins with the user uploading a contract. It provides the user with the ability to photograph or send the contract using an information terminal. Smartphones and tablets are commonly used as information terminals.

[0670] The received data is processed on the server by an information processing device. The server analyzes this data using natural language processing. Specifically, it uses the Google Cloud Natural Language API to analyze the clauses within the contract and perform natural language processing to understand their structure. Through this analysis, each segment within the contract is identified, and risk factors are extracted.

[0671] Next, based on the analyzed data, risk factors are evaluated using machine learning methods, and legal advice is generated. In this step, a machine learning model built using TensorFlow identifies high-risk elements by comparing them with a legal database and creates necessary advice. This legal advice is generated as appropriate by a generative AI model.

[0672] The generated legal advice is provided to the user via an output device. The user can view this information on their smartphone or computer screen and make any necessary corrections.

[0673] For example, if a company enters into a sales contract for a new product and the contract term clause is inappropriate, the system will provide legal advice such as, "The contract term is ambiguous. Please specify a concrete term." An example of a prompt to the generating AI model would be, "Identify potentially problematic clauses in the contract and assess the legal risks."

[0674] This system enables users to quickly and efficiently assess the legal risks of contracts, leading to increased efficiency in legal work and improved risk management.

[0675] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0676] Step 1:

[0677] The user uses a terminal to take a picture of the contract or select an existing electronic file and upload it to the system. The input is the captured image data or electronic document, and the output is the data sent to the server.

[0678] Step 2:

[0679] The server checks the format of the received files and converts different formats, such as PDFs, Word files, and image data, into a format that can be parsed. The input is data sent by the user, and the output is text data that is easy to process with natural language processing.

[0680] Step 3:

[0681] The server uses the Google Cloud Natural Language API to analyze text data, identify each clause within the contract, and perform natural language processing. The input is a text-based contract, and the output is a collection of sentences divided into chapters and sections.

[0682] Step 4:

[0683] The server evaluates risk factors using a machine learning model based on the analysis results, employing TensorFlow. The input is the contract details, divided by chapter and section, and the output is a list of identified risk factors.

[0684] Step 5:

[0685] The server generates legal advice based on risk factors. It utilizes a generative AI model to create specific advice. The input is a list of risk factors, and the output is a written legal advice document.

[0686] Step 6:

[0687] The server generates legal advice and sends it to the terminal, providing it to the output device for user review. The input is the legal advice text, and the output is the advice message displayed on the user's terminal.

[0688] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0689] This invention is a system in which a user uploads a contract via a terminal, and AI technology is used not only to assess the legal risks but also to recognize the user's emotions and adjust the advice accordingly. The operation of this system's program is described below.

[0690] Users select the contract they want to review via their device and upload it to the system. The device checks the format of the contract file (such as PDF or Word) and converts it to a format suitable for analysis as needed. This enables efficient analysis of the contract content using a natural language processing engine.

[0691] The server analyzes the converted electronic document using a natural language processing engine, segmenting the contract content and identifying risk elements based on the clauses. The AI ​​model generates legal advice regarding the identified risks based on legal databases and case law. The server then sends this advice to a display device so that the user can view it on their device.

[0692] Furthermore, the emotion engine recognizes the user's emotional state based on their interaction with the system. Specifically, it analyzes the context and typing speed of the user's questions, and, if voice input is available, the tone of their voice. Based on these recognition results, the server adjusts the wording of the generated legal advice. For example, if the user is feeling stressed, the system will provide simple and clear advice.

[0693] Furthermore, based on the emotions the emotion engine recognizes, it can personalize and provide relevant additional information and suggestions. For example, if a user expresses anxiety, the server can provide links to frequently asked questions (FAQs) or beginner's guides.

[0694] This system not only allows for proper management of legal risks, but also enables the provision of support tailored to the user's emotional state, resulting in a more personalized user experience.

[0695] The following describes the processing flow.

[0696] Step 1:

[0697] Users use their devices to select contract files to be reviewed and upload them to the system. PDF and Word files are commonly used as contract formats.

[0698] Step 2:

[0699] The terminal sends the uploaded electronic document to the server. The server receives the electronic document and converts it into a format that can be processed. For example, it prepares data for analysis by converting a PDF to text format.

[0700] Step 3:

[0701] The server uses a natural language processing engine to analyze the contract. This analysis divides the document into clauses and identifies confidentiality clauses and limitation of liability clauses. Each segment within the document is tagged, and risks from a legal perspective are extracted.

[0702] Step 4:

[0703] The AI ​​on the server references a legal database and assesses risk factors. Based on pre-trained data, the AI ​​model generates legal advice regarding the risks. For example, if the deadline setting is inappropriate, it might generate advice such as, "We recommend reviewing the protection period settings."

[0704] Step 5:

[0705] The server activates the emotion engine and analyzes the user's emotional state. Based on the user's interactions and input data, it evaluates emotions such as stress and anxiety. If voice input is available, voice tone is also used in the analysis.

[0706] Step 6:

[0707] The server adjusts the generated legal advice to suit the user's emotional state. For example, if the user is expressing anxiety, it prioritizes presenting concise and clear advice.

[0708] Step 7:

[0709] The server sends the adjusted legal advice to the device. The device displays the received advice to the user, who can review it and revise the contract. Personalized suggestions and relevant information based on emotions are also provided.

[0710] (Example 2)

[0711] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0712] Traditional contract analysis systems provide the functionality to legally evaluate the content of documents and identify risks, but they are unable to provide information tailored to the user's emotional state. Therefore, they fail to meet the need to alleviate user anxiety and stress and to offer more personalized advice.

[0713] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0714] In this invention, the server includes means for receiving an electronic document using an information processing device and analyzing the electronic document using a natural language processing engine; means for recognizing the user's emotional state based on user input information and adjusting the expression of legal advice based on the recognized emotional state; and means for providing additional or relevant information according to the user's emotional state. This makes it possible to provide personalized advice tailored to the user's emotional state.

[0715] An "information processing device" is a computer system that receives electronic documents and performs specified processing.

[0716] An "electronic document" is document data composed in a digital format, including file formats such as contracts and reports.

[0717] A "natural language processing engine" is a software technology that analyzes human language and converts it into a format that a computer can understand.

[0718] "Risk elements" refer to parts of an electronic document that are legally important and contain uncertainties or potential problems.

[0719] "Legal advice" refers to guidance and support information provided based on legal standards regarding actions users should take and considerations they should make in specific situations.

[0720] A "display device" is a hardware device, such as a screen or monitor, used to provide information to a user visually.

[0721] "Emotional state" refers to the mental or emotional response a user exhibits at a particular point in time during an interaction.

[0722] "Additional information" refers to supplementary information provided to the user, including data and materials to facilitate a more detailed understanding.

[0723] To implement this invention, the user first selects the contract document they wish to review using a terminal and uploads it electronically to the system. The terminal checks the format of the uploaded document and, if necessary, uses an automatic conversion tool to convert it into a data format that can be properly analyzed, and then sends it to the server as an electronic document. For example, this process includes converting a PDF to a text format.

[0724] The server analyzes received electronic documents using a natural language processing engine. This engine divides the document into segments and identifies the legal elements contained within each segment. An AI model then generates legal advice for the identified risks based on legal databases and past case precedents. This AI model has accumulated a wealth of data on common risks and their countermeasures, enabling sophisticated assessments.

[0725] Once the analysis and advice generation are complete, the server uses an emotion engine to recognize the user's emotional state based on factors such as input speed and tone of voice. Based on this, the server adjusts the wording of the generated legal advice according to the user's emotional state. Furthermore, the server can also provide additional relevant information and legal information to help the user better understand the situation.

[0726] As a concrete example, consider a case where a user uploads a lease agreement. This system would have a server identify important clauses and risks related to the lease agreement (e.g., termination clauses) and provide the user with simple and clear advice. If the server determines that the user is showing anxiety or stress, it would also provide links to beginner-friendly guide information to help them understand with confidence.

[0727] An example of a prompt for using a generative AI model is, "I have uploaded a residential lease agreement. Please assess the risks associated with termination and provide simple advice to users who are feeling stressed." This prompt allows the system to provide assessments and advice tailored to specific user requests.

[0728] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0729] Step 1:

[0730] The user uses a terminal to select the contract they want to review and upload it to the system. The input is the contract file, and the output is the transfer of the file to the system. Specifically, the user selects the contract through a file selection dialog and presses the "Upload" button.

[0731] Step 2:

[0732] The terminal checks the format of the uploaded contract. The input is the file uploaded by the user, and the output is its conversion into a parseable data format. Specifically, if a contract in PDF format is uploaded, the automatic conversion tool converts it to text format and saves it as a string.

[0733] Step 3:

[0734] The server passes the converted electronic document to a natural language processing engine for analysis. The input is contract data converted to text format, and the output is segmented content of the contract. Specifically, the natural language processing engine syntactically analyzes the contract and extracts important keywords and phrases for each clause.

[0735] Step 4:

[0736] The server uses an AI model to assess the legal risks of segmented contract content and generate advice. The input consists of segmented contract content and information from a legal database; the output is legal advice. Specifically, the AI ​​model determines the degree of risk and documents the advice based on relevant case law and regulations.

[0737] Step 5:

[0738] The server sends the generated advice to the terminal, which then displays the advice on its screen. The input is advice data generated by the AI ​​model, and the output is advice information presented visually to the user. Specifically, the terminal displays the advice in a text window so that the user can easily review it.

[0739] Step 6:

[0740] The device uses an emotion engine to recognize the user's emotional state. Input is the user's operation patterns and voice input (if any), and output is recognized emotion data. Specifically, it analyzes input speed and voice tone to determine whether the user is feeling anxious or stressed.

[0741] Step 7:

[0742] The server adjusts the wording of advice based on the emotional state and provides additional information as needed. The input is emotional data obtained from the emotion engine, and the output is adjusted legal advice and additional information. Specifically, it provides a link to a beginner's guide for users who are showing anxiety.

[0743] (Application Example 2)

[0744] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0745] In reviewing contracts and terms and conditions, traditional systems only assess legal risks, lacking support that considers the user's emotions and stress levels. This makes it difficult for users to confidently understand contract terms and make risk assessments. Furthermore, the lack of appropriate advice tailored to their emotional state results in an inadequate user experience.

[0746] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0747] In this invention, the server includes means for receiving an electronic document using an information processing device and analyzing the electronic document using a natural language processing engine; means for identifying risk elements and generating legal advice based on the analyzed content; means for outputting the generated legal advice to a display device; means for analyzing the user's emotions and adjusting the expression of the legal advice; and means for providing personalized additional information and guidance based on the user's emotional state. This enables the user to accurately understand legal risks in contract review and receive personalized advice that takes their emotions into consideration.

[0748] An "information processing device" is a device that receives electronic documents and has the function of analyzing those documents using a natural language processing engine.

[0749] A "natural language processing engine" is software that takes electronic documents as input data, analyzes their content, and structures it.

[0750] A "risk element" refers to a part of a document, such as a contract, that may contain legal problems or uncertainties.

[0751] "Legal advice" refers to information that provides instructions and advice on legal risks and countermeasures based on the content of the analyzed contract.

[0752] A "display device" is a device used to present generated legal advice to the user.

[0753] "Analyzing user emotions" is the process of inferring a user's emotional state based on their interactions.

[0754] "Personalizing and providing additional information based on emotional state" means selecting and individually presenting appropriate information and support according to the user's emotions.

[0755] This invention is a system that provides risk assessment for electronic documents such as contracts and terms of service, and offers advice tailored to the user's emotions. This system mainly consists of a server, terminals, a natural language processing engine, and an emotion analysis engine.

[0756] Users first upload electronic documents using a device such as a smartphone or computer. During this process, the device identifies the format of the electronic document and converts it to a parsable format as needed. It includes conversion functions that support formats such as PDF and Word files.

[0757] Subsequently, the server analyzes the electronic document using a natural language processing library based on Python (e.g., spaCy or NLTK). It segments the content of the document and identifies risk elements. Furthermore, it refers to legal databases and generates legal advice for the identified risks.

[0758] The generated advice is output to the terminal's display device, and at the same time, the user's interaction is analyzed by an emotion analysis engine. This emotion analysis utilizes machine learning models such as TensorFlow and PyTorch to estimate the user's stress and anxiety levels by evaluating the user's input speed and voice tone.

[0759] Based on the user's emotional state, the generative AI model provides simple and clear advice and offers additional support information. For example, for users who show anxiety, it displays links to FAQs and beginner's guides.

[0760] As a concrete example, consider a scenario where a user uploads a contract related to a new service's pricing plan and wants to know about the risks associated with a specific clause. In this case, the prompt might be, "I want to know what risks are involved in this specific clause of the contract." This system can analyze the relevant clause and directly inform the user, for example, "This clause carries the risk of recurring charges."

[0761] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0762] Step 1:

[0763] The user selects an electronic document using a terminal and uploads it to the system. In this step, the format of the electronic document (PDF or Word file) is input, and processing is performed to convert it into a parsable format. Specifically, the terminal identifies the file format and converts it to text format as needed. Text data is obtained as output.

[0764] Step 2:

[0765] The server receives the converted text data. A natural language processing engine (e.g., spaCy) analyzes the text data and segments the content of the contract. At this stage, legally important phrases and risk elements are extracted from the input text data. The output is structured data containing the risk elements.

[0766] Step 3:

[0767] The server uses a legal database to generate legal advice based on identified risk factors. Here, risk factors are given as input, and data processing is performed to retrieve corresponding legal information. The output is legal advice to be provided to the user.

[0768] Step 4:

[0769] The server sends the generated legal advice to the terminal's display device. The user can review this, and their interaction at that time leads to the next process. The input is the generated legal advice, and the output is the advice displayed on the user's screen.

[0770] Step 5:

[0771] The emotion analysis engine is activated to analyze the user's interaction data (such as input speed and voice tone). In this step, data calculations are performed to infer the user's emotional state from the input interaction data. The inferred emotional state is obtained as the output.

[0772] Step 6:

[0773] The server utilizes a generative AI model to adjust the wording of advice based on the user's emotional state. Furthermore, it provides additional support information (such as FAQ links) as needed. Input is the result of the emotional analysis and legal advice; output is the adjusted advice and support information.

[0774] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0775] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0776] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0777] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0778] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0779] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0780] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0781] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0782] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0783] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0784] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0785] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0786] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0787] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0788] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0789] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0790] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0791] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0792] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0793] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0794] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0795] The following is further disclosed regarding the embodiments described above.

[0796] (Claim 1)

[0797] An information processing device provides means for receiving an electronic document and analyzing the electronic document using a natural language processing engine,

[0798] Based on the analyzed information, a means to identify risk factors and generate legal advice,

[0799] A means for outputting the generated legal advice to a display device,

[0800] A system that includes this.

[0801] (Claim 2)

[0802] The system according to claim 1, which identifies the format of an electronic document and converts it into an appropriate processing format.

[0803] (Claim 3)

[0804] The system according to claim 1, which provides the user with additional information and relevant legal information based on the generated legal advice.

[0805] "Example 1"

[0806] (Claim 1)

[0807] An information processing device provides means for receiving an electronic document, identifying its format, and converting it into an appropriate processing format.

[0808] A method for segmenting converted electronic documents by clause using a natural language processing engine, tagging them, and analyzing them,

[0809] A means of accessing legal databases, utilizing machine learning modules, identifying risk components based on the analyzed content, and generating legal advice,

[0810] A means for transmitting the generated legal advice to a user device via a communication device and outputting it to a display device,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, which provides the user with additional information and relevant legal information based on the generated legal advice.

[0814] (Claim 3)

[0815] The system according to claim 1, wherein the machine learning module is pre-trained with data from experts with specialized knowledge.

[0816] "Application Example 1"

[0817] (Claim 1)

[0818] An information processing device receives data and analyzes the data using natural language processing means.

[0819] Based on the analyzed information, a means to identify risk factors and generate legal advice,

[0820] Means for providing the generated legal advice to the output device,

[0821] A means of providing a function for users to photograph or transmit contracts using an information terminal,

[0822] A means for automatically analyzing relevant documents and generating additional information based on risk assessment results,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, which determines the type of electronic document and converts it into an appropriate, processable format.

[0826] (Claim 3)

[0827] The system according to claim 1, which presents the user with detailed information and relevant bill data based on the generated legal advice.

[0828] "Example 2 of combining an emotion engine"

[0829] (Claim 1)

[0830] An information processing device provides means for receiving an electronic document and analyzing the electronic document using a natural language processing engine,

[0831] Based on the analyzed information, a means to identify risk factors and generate legal advice,

[0832] A means for outputting the generated legal advice to a display device,

[0833] A means of recognizing the emotional state based on user input information and adjusting the expression of legal advice based on the recognized emotional state,

[0834] Means for providing additional or related information according to the user's emotional state,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, which identifies the format of an electronic document and converts it into an appropriate processing format.

[0838] (Claim 3)

[0839] The system according to claim 1, which provides the user with additional information and relevant legal information based on the generated legal advice.

[0840] "Application example 2 when combining with an emotional engine"

[0841] (Claim 1)

[0842] An information processing device provides means for receiving an electronic document and analyzing the electronic document using a natural language processing engine,

[0843] Based on the analyzed information, a means to identify risk factors and generate legal advice,

[0844] A means for outputting the generated legal advice to a display device,

[0845] A means of analyzing user sentiment and adjusting the wording of legal advice,

[0846] A means of providing personalized additional information and guidance based on the user's emotional state,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1, which identifies the format of an electronic document and converts it into an appropriate processing format.

[0850] (Claim 3)

[0851] The system according to claim 1, which provides the user with additional information and relevant legal information based on the generated legal advice, and dynamically presents support information according to the user's emotional state. [Explanation of Symbols]

[0852] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An information processing device receives data and analyzes the data using natural language processing means. Based on the analyzed information, a means to identify risk factors and generate legal advice, Means for providing the generated legal advice to the output device, A means of providing a function for users to photograph or transmit contracts using an information terminal, A means for automatically analyzing relevant documents and generating additional information based on risk assessment results, A system that includes this.

2. The system according to claim 1, which determines the type of electronic document and converts it into an appropriate, processable format.

3. The system according to claim 1, which presents the user with detailed information and data on relevant legislation based on the generated legal advice.

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